CN113484453B - Cerebral arterial thrombosis early warning method - Google Patents
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Abstract
The invention discloses an ischemic stroke early warning method, and belongs to the field of medicine. The serum sample is analyzed based on the UPLC-Q/TOF-MS technology and a metabonomics method, the candidate biomarker of the atherosclerosis-induced ischemic stroke is screened out, and the candidate biomarker is combined with the atherosclerosis index to early warn the ischemic stroke. The invention determines potential biomarkers in 5 atherosclerosis-induced ischemic stroke blood serum by multivariate statistical analysis, and realizes the early warning function for diagnosis and treatment of ischemic stroke by combining the potential biomarkers and the atherosclerosis index.
Description
Technical Field
The invention relates to the field of medicine, in particular to an ischemic stroke early warning method, and specifically relates to a method for early warning ischemic stroke by combining an atherosclerosis index and a serum metabolite.
Background
Stroke, also known as stroke and cerebrovascular accident, is an acute cerebral blood circulation disorder caused by stenosis, occlusion or rupture of internal cerebral arteries due to various inducing factors. Stroke is currently one of the major diseases threatening the global human health and has become the second most fatal disease in the world. The cerebral apoplexy IS divided into Ischemic Stroke (IS) and hemorrhagic stroke, and the onset of the ischemic stroke IS mainly used. Ischemic stroke refers to ischemic necrosis or brain softening disease of a localized brain tissue caused by blood supply disorder, ischemia, and hypoxia in the brain. Ischemic stroke mainly originates from atherosclerosis, and further initiates a series of pathological processes such as brain tissue energy metabolism disorder, excitatory amino acid toxicity effect, oxidation/nitrification stress, inflammatory reaction, apoptosis, autophagy and the like, and about one third of patients suffering from atherosclerosis in ischemic stroke. Research shows that atherosclerosis is the cause of inducing serious diseases such as cerebral infarction, cerebral hemorrhage, coronary heart disease and the like. The development and progression of atherosclerosis is a slowly progressive process, usually progressing for years or decades. Thus, in general, most patients do not have obvious symptoms or signs of abnormality, and the plasma lipoprotein levels are found to be elevated in most patients who have undergone biochemical blood tests for other reasons. However, atherosclerotic complications are sudden and often non-predictive. Therefore, blood metabolites are required to be searched as a prediction index of atherosclerosis-induced stroke.
Studies have shown that elevated serum Total Cholesterol (TC), Triacylglycerols (TG), low density lipoprotein cholesterol (LDL-C) and reduced serum high density lipoprotein cholesterol (HDL-C) are risk factors for atherosclerosis. However, a single dyslipidemia component is not effective in assessing atherosclerosis. In recent years, researchers have proposed the concept of the Atherosclerosis Index (AI), which is defined as the ratio of TC to HDL-C, i.e., the Atherosclerosis Index (AI) ([ Total Cholesterol (TC) -high density lipoprotein (HDL-C) ]/high density lipoprotein cholesterol (HDL-C)). AI has been reported in the literature to be positively correlated with atherosclerosis severity. Its normal value is that the Atherosclerosis Index (AI) is less than 4, if the Atherosclerosis Index (AI) is less than 4, it reflects that the degree of arteriosclerosis is not serious or is being reduced, and the smaller the value, the lighter the degree of arteriosclerosis is, and the lower the risk of causing cardiovascular and cerebrovascular diseases is; if the arteriosclerosis index is more than 4, it is said that arteriosclerosis has occurred, and the larger the value, the more severe the degree of arteriosclerosis, and the higher the risk of developing cardiovascular and cerebrovascular diseases.
Metabolomics (metagenomics or metagenomics) is a subject that grows after proteomics and genomics, and obtains a metabolic network of a biological system by observing dynamic changes of metabolites before and after the biological system is stimulated or disturbed and applying various analysis tools. Metabonomics is widely used in various scientific fields due to its characteristics of high-pass, integrity, high sensitivity and high selectivity. In the background of system biology, metabonomics qualitatively and quantitatively analyze the change of endogenous metabolites of various organisms to evaluate the pathophysiological stimulation of organisms, and the metabonomics is an important analysis means for researching clinical metabolism.
Ischemic stroke is a serious public health problem worldwide, and seriously threatens the life health of human beings. Atherosclerosis is the cause of stroke, and the occurrence and progression of atherosclerosis is a slowly progressive process, but is usually not precautionary due to its complications. Metabolic disorder is considered as a key event causing ischemic stroke, so that the method for early warning of ischemic stroke is provided based on relevant research and understanding of stroke in the prior art, and has great significance for diagnosis and treatment of ischemic stroke.
Disclosure of Invention
The invention aims to provide an ischemic stroke early warning method, which aims to solve the problems in the prior art, determines 5 potential biomarkers in atherosclerosis-induced ischemic stroke serum through multivariate statistical analysis, and provides an early warning effect for diagnosing and treating ischemic stroke by combining the potential biomarkers with an atherosclerosis index.
In order to achieve the purpose, the invention provides the following scheme:
the invention provides an ischemic stroke early warning method, which is characterized in that a serum sample is analyzed based on a UPLC-Q/TOF-MS technology and a metabonomics method, a candidate biomarker of atherosclerosis-induced ischemic stroke is screened out, and the candidate biomarker is combined with an atherosclerosis index to early warn the ischemic stroke.
Preferably, the early warning method specifically comprises the following steps:
step 1: collecting serum samples, randomly divided into S1 group and S2 group, wherein the S1 group of serum samples constitutes a discovery phase and the S2 group of serum samples constitutes a validation phase;
step 2: analyzing the S1 group serum samples in the discovery stage based on UPLC-Q/TOF-MS technology and metabonomics method, and screening candidate biomarkers of atherosclerosis-induced ischemic stroke;
and step 3: verifying the candidate biomarkers by using an S2 group of serum samples, and analyzing to obtain potential biomarkers of atherosclerosis-induced ischemic stroke;
and 4, step 4: the potential biomarker and a clinical index atherosclerosis index are subjected to combined analysis, and an early warning method is provided for ischemic stroke in the aspect of metabonomics.
Preferably, the candidate biomarkers in step 2 comprise: 2,2, 2-trichloroethanol, 1-methylpyrrolidine, thiomorpholine-3-carboxylate, 2, 4-dimethyl-1- (1-methylethyl) -benzene, SM (18:0/14:0), LysoPC (18:0/0:0), PE-NMe (18:1/22:1), PC (18:0/18:0) and PC (18:2/18: 2).
Preferably, the potential biomarkers in step 3 comprise: 1-methylpyrrolidine, SM (18:0/14:0), PC (18:0/18:0), LysoPC (18:0/0:0) and PC (18:2/18: 2).
Preferably, the clinical index atherosclerosis index in step 4 is obtained by performing non-targeted metabolomic detection analysis on serum samples from different sources including atherosclerosis index <4, atherosclerosis index >4, ischemic stroke combined atherosclerosis index <4 and ischemic stroke combined atherosclerosis index > 4.
Preferably, at an atherosclerotic index >4, 1-methylpyrrolidine, SM (18:0/14:0) and PC (18:0/18:0) are in a downward trend, and LysoPC (18:0/0:0) and PC (18:2/18:2) are in an upward trend;
when the atherosclerosis index is more than 4, the combination of the down-regulated 1-methyl pyrroline and the up-regulated LysoPC (18:0/0:0) and PC (18:2/18:2) can early warn atherosclerosis-induced ischemic stroke.
The invention discloses the following technical effects:
the invention provides an ischemic stroke early warning method, which adopts UPLC-Q-TOF/MS metabonomics technology to measure serum metabolic spectrum, and adopts methods such as multivariate statistics, VIP value test, statistical test and the like to find key diagnosis biomarkers of atherosclerosis induced ischemic stroke. Through experimental verification, 5 potential biomarkers exist in the screened candidate markers: 1-methylpyrroline, SM (18:0/14:0), PC (18:0/18:0), LysoPC (18:0/0:0) and PC (18:2/18:2), in subjects with an atherosclerotic index >4, 1-methylpyrroline, SM (18:0/14:0) and PC (18:0/18:0) are in a downward trend and LysoPC (18:0/0:0) and PC (18:2/18:2) are in an upward trend. Among them, 1-methylpyrrolidine, LysoPC (18:0/0:0), PC (18:0/18:0) and the atheromatous index are closely related. The atherosclerosis index is more than 4, so that an early warning effect can be provided for diagnosis of ischemic stroke, the down-regulated 1-methylpyrrolidine, LysoPC (18:0/0:0) and up-regulated PC (18:0/18:0) are combined, the biomarker for predicting atherosclerosis-induced ischemic stroke can be used, and a basis is further provided for prevention and accurate treatment of atherosclerosis-induced stroke.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of a method for warning ischemic stroke according to the present invention;
FIG. 2 is a principal component analysis score plot of a discovery set and a validation set in accordance with the present invention; a, finding a PCA score chart of a set; b, verifying a PCA score chart of the set; c: variable wain diagram showing VIP value > 1 over two main groups (lvs.g and gvs.sg); d: l vs G PLS-DA analysis; e: g vs SG PLS-DA analysis; f: l vs SL PLS-DA analysis; g, L vs G replacement inspection; g vs SG replacement test; l vs SL displacement test;
FIG. 3 is a ROC curve and a heat map analysis established for metabolite information of 5 potential biomarkers of the invention; a: finding a set ROC curve result; b, verifying a set ROC curve result; thermographic analysis of 5 markers;
FIG. 4 is a diagram of a validation set metabolic marker pathway analysis according to the present invention;
FIG. 5 is a scatter plot analysis of 5 markers of the present invention; a is 1-Methylpyrrololinium; b: SM (18:0/14:0) C: LysoPC (18:0/0: 0); d is PC (18:0/18: 0); e PC (18:2/18: 2).
Detailed Description
Reference will now be made in detail to various exemplary embodiments of the invention, the detailed description should not be construed as limiting the invention but rather as a more detailed description of certain aspects, features and embodiments of the invention.
It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Further, for numerical ranges in this disclosure, it is understood that each intervening value, between the upper and lower limit of that range, is also specifically disclosed. Every smaller range between any stated value or intervening value in a stated range and any other stated or intervening value in a stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although only preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention. All documents mentioned in this specification are incorporated by reference herein for the purpose of disclosing and describing the methods and/or materials associated with the documents. In case of conflict with any incorporated document, the present specification will control.
It will be apparent to those skilled in the art that various modifications and variations can be made in the specific embodiments of the present disclosure without departing from the scope or spirit of the disclosure. Other embodiments will be apparent to those skilled in the art from consideration of the specification. The specification and examples are exemplary only.
As used herein, the terms "comprising," "including," "having," "containing," and the like are open-ended terms that mean including, but not limited to.
Materials and methods
1. Laboratory apparatus and reagent
Distilled water (Guangzhou Drech food & beverage Co., Ltd.), chromatographically pure acetonitrile (Oceanpak Co., Sweden), chromatographically pure formic acid (ROE Co., USA) low temperature high speed centrifuge (Changshan instruments centrifuge instruments Co., Ltd.), JKYMEN ultrasonic cleaner, small-sized oscillatory vortexer (Haimen, Linebel instruments Co., Ltd.), Waters Acquity UPLC liquid chromatograph (Waters Co., USA), Waters Xevo G2S Q-TOF mass spectrometer (Waters Co., USA), ACQUITY UPLC BEH C18 chromatographic column (2.1X 100mm, 1.7 μm, Waters Co., USA).
2. Experimental methods
(1) Experiment grouping
In the experiment, a total of 400 subjects were enrolled, including four groups of an atherosclerotic index <4(L), an atherosclerotic index >4(G), an ischemic stroke atherosclerotic index <4(SL) and an ischemic stroke atherosclerotic index >4 (SG). Serum samples were collected from 8 months 2019 to 12 months 2020, forming both the discovery and validation stages in this study. In the discovery stage, 160 samples are collected to form four groups of L, G, SL and SG; in the validation stage, 240 samples were collected, which consisted of L, G, SG groups.
The inclusion criteria for subjects with ischemic stroke are: (1) the product meets the diagnosis standard of western medicine cerebral apoplexy, is 18-80 years old, and meets any one or more of the following items. (2) Has a definite history of old cerebral infarction. (3) The brain has been diagnosed with ischemic stroke by CT/MRI.
The exclusion criteria for subjects with ischemic stroke were: (1) combined with other brain diseases, climacteric syndrome, hyperthyroidism, spinal cord type or vertebral artery type cervical spondylosis, gastroesophageal reflux disease or hiatal hernia and other diseases which can cause chest pain. (2) Severe cardiopulmonary insufficiency, severe cardiac arrhythmias (rapid atrial fibrillation, atrial flutter, paroxysmal ventricular tachycardia, etc.). (3) Renal dysfunction. (4) Complicated with severe primary diseases such as hematopoietic system or malignant tumor. (5) Pregnant women, lactating women or women of childbearing age who have childbearing requirements. (6) Psychotic subjects, or cognitive dysfunction. (7) Patients with severe metabolic diseases such as gouty nephropathy.
This experimental study was approved by each subject with the ethical committee of the first subsidiary hospital of tianjin medical university, with ethical code 18JCYBJC 94200. All participants provided informed consent and met the ethical guidelines of the declaration of helsinki 1975. Gender and age distribution were matched as closely as possible between groups, and details are given in table 1.
(2) Serum sample collection and processing
Samples were collected and stored immediately in a freezer at-80 ℃ and thawed in a 4 ℃ environment prior to analysis. Sucking 50 μ L of serum, adding 150 μ L of acetonitrile, performing ultrasonic treatment in ice water bath for 10min, mixing for 1min, centrifuging at 13000rpm and 4 deg.C for 15min, and collecting supernatant for UPLC-Q-TOF/MS analysis. The pretreatment of the quality control sample is parallel and identical to the treatment of the study sample. Quality control samples were inserted uniformly into each set of analytical run sequences to monitor the stability of the large scale analysis.
(3) UPLC-Q/TOF-MS analysis
This was done using a Waters Acquity UPLC liquid chromatograph (Waters Corp., USA) and a Waters Xevo G2SQ-TOF mass spectrometer (Waters Corp., USA). An ACQUITY UPLC BEH C18 column (2.1X 100mm, 1.7 μm, Waters Corp., USA) was used in ESI + mode. Detailed experimental conditions for UPLC separation and MS detection are as follows.
Chromatographic analysis conditions: column temperature: 45 ℃; flow rate: 0.3 mL/min; sample introduction amount: 5 μ L. Mobile phase composition: a: 0.1% formic acid and B: 0.1% formic acid acetonitrile. The elution gradient was: 0-0.5min, 1% B; 0.5-2min, 1% -50% B; 2-9min, 50% -99% B; 9-10min, 99% B; 10-10.5min, 99% -1% B; 10.5-12min, 1% B.
Mass spectrometry conditions: capillary voltage 2.0kV, ionization source temperature 100 ℃, dry gas flow rate 10mL/min, desolventizing flow rate 600L/D, desolventizing temperature 450 ℃, cone air flow rate: 50L/D, quadrupole scanning range m/z 50-1000.
(4) Data analysis
Data export and processing: the collected liquid quality information of each group is converted into a data file through a MassLynx V4.1 workbench (Waters corporation in America), and the data preprocessing is realized by using an R language technology to carry out reduction, missing value filling and the like. Multivariate analysis was subsequently performed using SIMCA-P14.1 (Umetrics AB, Umea, Sweden). Unsupervised principal component analysis was used to assess overall metabolome changes between groups and monitor the stability of the study. A supervised model of partial least squares discriminant analysis was performed to maximize the distance between groups and identify important variables that contribute significantly to classification from their important Variables (VIPs) in the projection. 200 permutation tests were performed to assess the risk of model overfitting.
Data statistical analysis and identification: and (3) performing normal analysis and variance analysis on the data by adopting a sps 22.0 statistical software, and further selecting an independent sample t test (approximate t test) or a non-parameter test to obtain a candidate differential marker related to the disease with significance variation (P < 0.05). The candidate biomarkers were retrieved from their m/z values in the HMDB database (http:// www.hmdb.ca /), and confirmed by MS/MS fragment information.
Data visualization and functional analysis: binary logistic regression is used to model based on potential biomarkers. The receiver operating characteristic is used to evaluate the results of the regression analysis. To show the results more clearly, heat and scatter plots of potential metabolic markers will be plotted using metamoloanalysts (Version 4.6.0) and GraphPad Prism (Version 8) to express the results of the correlation analysis. Meanwhile, pathway analysis based on differential metabolites revealed that important metabolic pathways are disturbed.
Second, result in
1. Demographics of study population
The workflow diagram of this invention is shown in fig. 1, and 160 serum samples were collected in the discovery group in order to define candidate biomarkers. 240 participants were recruited to validate these candidate biomarkers and define potential biomarkers in the validation set. Clinical information for all subjects is listed in table 1.
2. Serum metabolism profile
QC ion chromatograms extracted in discovery and validation set ES + mode. First, the data were phenotyped and the QC samples were clustered closely together in the principal component analysis score plots of the discovery and validation sets (fig. 2A, 2B), further confirming the reliability of the experiment. In addition, the trend of differentiation between samples from L, G, SL and SG groups indicated that there were significant systemic metabolic differences between these groups. These variables will be used for subsequent multivariate and univariate analysis.
3. Definition of potential metabolic biomarkers for atherosclerosis-induced ischemic stroke
First, we found a set partial least squares discriminant analysis score plot (fig. 2D,2E,2F), revealing a clear separation between these groups, and in the PLS-DA model of this study, R of L and G groups 2 X＝0.883，Q 2 0.776; r of G and SG groups 2 X＝0.849，Q 2 0.721; r of L and SL group 2 X＝0.906，Q 2 0.772, indicating that the experiment was establishedThe prediction model has good fitting conditions (fig. 2G,2H,2I), and the measurement result is stable and reliable. VIP on two major components based on the results given by the PLS-DA model>1.0 (fig. 2C) were further screened for important variables that contribute to the potential metabolic biomarkers identified as atherosclerosis-induced ischemic stroke.
The intersection data of the L and SL groups was used among 17 metabolites to exclude potential metabolic biomarkers that could affect atherosclerosis induced ischemic stroke, followed by orthodox and homogeneity of variance tests on these markers using SPSS 22.0, and the significance tests were performed using t-test, approximate t-test, Mann-Whitney U assay (Mann-Whitney U) based on the classification of test results, resulting in disease-related differential markers with significant changes (P < 0.05). And screening and searching and comparing the differential metabolites in an HMDB database by using the m/z values of the differential metabolites, and identifying and confirming the found differential metabolites according to an LC-MS/MS (liquid chromatography-mass spectrometry/mass spectrometry) map, metabolite database information and secondary ion fragment information provided in a search and collection literature. After the analysis, 9 candidate biomarkers are found (table 2), and after verification, 5 potential biomarkers of atherosclerosis-induced ischemic stroke are finally obtained.
4. Metabolic marker analysis
From the results of the metabolomics data processing described above (Table 2), 9 biomarkers were selected from the discovery set, and the levels of 9 metabolites were significantly altered in IS patients compared to G in the samples of the discovery set. The four metabolic markers of SM (18:0/14:0), 2, 4-dimethyl-1- (1-methylethyl) -benzene, 1-methylpyrrolidine and PC (18:0/18:0) are obviously reduced compared with a control group. LysoPC (18:0/0:0), thiomorpholine-3-carboxylate, 2,2, 2-trichloroethanol, PC (18:2/18:2) and PE-NMe (18:1/22:1) were significantly elevated compared to the control group. The obtained 5 biomarkers, namely SM (18:0/14:0), 1-methylpyrrolidine and PC (18:0/18:0), are significantly reduced compared with the control group in the validation set. Two metabolic markers of lysoPC (18:0/0:0) and PC (18:2/18:2) are obviously increased compared with the control group. It is noted that the differential biomarkers found are abundant in lipid substances, and when candidate biomarkers of atherosclerosis-induced ischemic stroke are screened, the differential markers obtained by intersecting the L group and the SL group do not intersect with 17 substances.
In order to verify whether the 5 biomarkers have an early warning effect, the early warning effect of the markers is analyzed, the early warning effect is judged by Area under the curve (AUC) corresponding to each curve, and the metabolite information of the 5 screened potential biomarkers is used for establishing a binary logistic regression model and an ROC curve. Finally, a combined diagnosis model of 5 markers is obtained (fig. 3A and 3B), the area under the curve AUC is 0.841 (discovery set), and the area under the curve AUC is 0.774 (verification set), which indicates that the combination of 5 metabolic markers has better early warning capability.
5. Correlation of potential biomarkers with clinical features and atherosclerotic index >4 induced ischemic stroke
Pathway analysis of 5 potential biomarkers (fig. 4) shows that metabolic disorders induced by atherogenic index >4 ischemic stroke are mainly related to glycerophospholipid metabolism, and in addition, affect pathways such as linoleic acid metabolism, alpha-linolenic acid metabolism, sphingolipid metabolism, arachidonic acid metabolism, and the like.
In the invention, in order to more intuitively observe the relative level change of the biomarkers in different groups, a hierarchical clustering analysis method, namely a heat map and a scatter diagram, is adopted to analyze key biomarkers for distinguishing. In the heat map, we can see the significant changes of the 5 metabolic markers obtained from the validation set among the three groups, with the trend shown in fig. 3C. It can also be found from the scatter plot (fig. 5) that the control group of 5 markers has significant difference from the other two groups.
To further reveal the relationship of these markers to the atherosclerotic index, the markers were analyzed in combination with the atherosclerotic index. The results show that 1-Methylpyrolinium, SM (18:0/14:0) and PC (18:0/18:0) are in a downward trend and LysoPC (18:0/0:0) and PC (18:2/18:2) are in an upward trend in subjects with an atherosclerotic index > 4. Among them, 1-Methylpyrrolidinium, LysoPC (18:0/0:0), PC (18:2/18:2) are closely related to the atheromatous index. The atherosclerosis index of more than 4 can provide an early warning effect for diagnosis of ischemic stroke, and the down-regulated 1-Methylpyrolinium and the up-regulated LysoPC (18:0/0:0) and PC (18:2/18:2) are combined to be used as biomarkers for predicting atherosclerosis-induced ischemic stroke, and further provide a basis for prevention and accurate treatment of atherosclerosis-induced stroke.
In conclusion, the potential biomarker of atherosclerosis-induced ischemic stroke is defined and verified through the research of a large sample, and the biomarker can effectively perform early warning on the atherosclerosis-induced ischemic stroke. In addition, the metabolome shows a certain advantage in the aspect of screening the marker, and the analysis of the marker of the invention finds that the proportion of lipid substances in the marker of atherosclerosis-induced ischemic stroke is larger. Therefore, metabolome in combination with lipidome analysis may also be considered to more accurately discover biomarkers that are potential for disease.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (1)
1. The application of the biomarker in the preparation of an ischemic stroke early warning product is characterized in that a serum sample is analyzed based on a UPLC-Q/TOF-MS technology and a metabonomics method, a candidate biomarker of atherosclerosis-induced ischemic stroke is screened out, and the candidate biomarker is combined with an atherosclerosis index to early warn the ischemic stroke; the candidate biomarkers include: 2,2, 2-trichloroethanol, 1-methylpyrrolidine, thiomorpholine-3-carboxylate, 2, 4-dimethyl-1- (1-methylethyl) benzene, SM (18:0/14:0), LysoPC (18:0/0:0), PE-NMe (18:1/22:1), PC (18:0/18:0) and PC (18:2/18: 2);
the early warning method specifically comprises the following steps: step 1: collecting serum samples, randomly divided into S1 group and S2 group, wherein the S1 group of serum samples constitutes a discovery phase and the S2 group of serum samples constitutes a validation phase;
step 2: analyzing the S1 group serum samples of the discovery stage based on UPLC-Q/TOF-MS technology and metabonomics method, and screening candidate biomarkers of atherosclerosis-induced ischemic stroke;
and step 3: verifying the candidate biomarkers by using an S2 group of serum samples, and analyzing to obtain potential biomarkers of atherosclerosis-induced ischemic stroke;
and 4, step 4: the potential biomarker and a clinical index atherosclerosis index are subjected to combined analysis, and an early warning method is provided for ischemic stroke in the aspect of metabonomics;
the potential biomarkers in step 3 include: 1-methylpyrrolidine, SM (18:0/14:0), PC (18:0/18:0), LysoPC (18:0/0:0) and PC (18:2/18: 2);
the clinical index in step 4, namely the atherosclerosis index, is obtained by carrying out non-targeted metabonomics detection analysis on serum samples of different sources, wherein the serum samples comprise an atherosclerosis index <4, an atherosclerosis index >4, an ischemic stroke combined atherosclerosis index <4 and an ischemic stroke combined atherosclerosis index > 4;
when the atherosclerosis index is more than 4, the 1-methyl pyrroline, the SM (18:0/14:0) and the PC (18:0/18:0) are in a descending trend, and the LysoPC (18:0/0:0) and the PC (18:2/18:2) are in an ascending trend;
when the atherosclerosis index is more than 4, the combination of the down-regulated 1-methylpyrrolidine and the up-regulated LysoPC (18:0/0:0) and PC (18:2/18:2) can warn atherosclerosis to induce ischemic stroke.
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